A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
Possible theoretical limits on holographic quintessence from weak gravity conjecture
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abstract
The holographic dark energy model is one of the important ways for dealing with the dark energy problems in the quantum gravity framework. In this model, the dimensionless parameter $c$ plays an essential role in determining the evolution of the holographic dark energy. In particular, the holographic dark energy with $c\geq 1$ can be effectively described by a quintessence scalar-field. However, according to the requirement of the weak gravity conjecture the variation of the quintessence scalar-field should be less than the Planck mass, which would give theoretic constraints on the parameters $c$ and $\Omega_{\mathrm{m0}}$. Therefore, we get the possible theoretical limits on the parameter $c$ for the holographic quintessence model.
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.